Applying consequence-driven scenario selection to lifelines

Abstract

We present a new consequence-driven framework for earthquake scenario selection. For emergency managers, utility operators, policy makers, and other stakeholders, a scenario-based seismic risk assessment is often necessary for the purpose of emergency management and planning. In developing a scientifically defensible scenario, stakeholders can simulate a realistic event in order to pre-identify vulnerabilities in the system and support action to address these vulnerabilities. Selecting scenarios is particularly challenging for important population centers and critical infrastructure in stable tectonic environments, such as in the central and eastern United States, where uncertain long-term seismicity and unknown faults offer inadequate constraints. Notably, significant events in these so-called stable regions do occur (e.g., Nahanni, Canada, 1985, M6.9; Tennant Creek, Australia, 1998, M6.7). In regions of low seismicity, even moderate events can be consequential due to the higher vulnerability of buildings typical of such regions when compared to regions of higher seismicity. Furthermore, communicating seismic risk to stakeholders and the general public in these regions can be especially challenging due to the complexities of characterizing the hazard level. This framework has been developed to address these challenges for scenario selection in low seismic hazard regions. In this new approach, the analysis begins instead with the explicit definition of a consequence of concern to the specific stakeholder. This can range from a definition of loss (in lives, dollars, or another metric of interest), or a performance metric for critical infrastructure. The framework leverages United States Geological Survey software to run the hazard and consequence analysis. Driven by this stakeholder-defined consequence, an inversion analysis generates a complete event set of candidate scenarios that could breach this consequence. The final selection of a scenario, or family of scenarios, is then scientifically informed, but not limited by our lack of constraints in characterizing the hazard.